### pandas-videos - Jupyter notebook and datasets from the pandas Q&A video series

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Read about the series, and view all of the videos on one page: Easier data analysis in Python with pandas.

http://bit.ly/pandas-videos
https://github.com/justmarkham/pandas-videos

 Tags data-science jupyter-notebook pandas tutorial data-analysis data-cleaning Implementation Jupyter Notebook License Public Platform

## 100-pandas-puzzles - 100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)

•    Jupyter

Inspired by 100 Numpy exerises, here are 100* short puzzles for testing your knowledge of pandas' power. Since pandas is a large library with many different specialist features and functions, these excercises focus mainly on the fundamentals of manipulating data (indexing, grouping, aggregating, cleaning), making use of the core DataFrame and Series objects. Many of the excerises here are straightforward in that the solutions require no more than a few lines of code (in pandas or NumPy - don't go using pure Python!). Choosing the right methods and following best practices is the underlying goal.

## statistical-analysis-python-tutorial - Statistical Data Analysis in Python

•    HTML

Chris Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia. This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.

## data-forge-ts - The JavaScript data transformation and analysis toolkit inspired by Pandas and LINQ.

•    TypeScript

The JavaScript data transformation and analysis toolkit inspired by Pandas and LINQ. Implemented in TypeScript, used in JavaScript ES5+ or TypeScript.

## python-for-data-analysis - An introduction to data science using Python and Pandas with Jupyter notebooks

•    Jupyter

Course in data science. Learn to analyze data of all types using the Python programming language. No programming experience is necessary. Note: O'Reilly Media titles are free to UCSD affiliates with Safari Books Online.

## xarray - N-D labeled arrays and datasets in Python

•    Python

xarray (formerly xray) is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the Common Data Model for self- describing scientific data in widespread use in the Earth sciences: xarray.Dataset is an in-memory representation of a netCDF file.

## practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

•    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

## pandas_exercises - Practice your pandas skills!

•    Jupyter

Fed up with a ton of tutorials but no easy way to find exercises I decided to create a repo just with exercises to practice pandas. Don't get me wrong, tutorials are great resources, but to learn is to do. So unless you practice you won't learn. My suggestion is that you learn a topic in a tutorial or video and then do exercises. Learn one more topic and do exercises. If you got the answer wrong, don't go directly to the solution with code.

## pandas-cookbook - Recipes for using Python's pandas library

•    Jupyter

pandas is a Python library for doing data analysis. It's really fast and lets you do exploratory work incredibly quickly. The goal of this cookbook is to give you some concrete examples for getting started with pandas. The docs are really comprehensive. However, I've often had people tell me that they have some trouble getting started, so these are examples with real-world data, and all the bugs and weirdness that entails.

## data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines

•    Python

IPython Notebook(s) demonstrating deep learning functionality.IPython Notebook(s) demonstrating scikit-learn functionality.

## PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks

•    Jupyter

This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Run the code using the Jupyter notebooks available in this repository's notebooks directory.

## Jupyter - Web-based notebook environment for interactive computing

•    Python

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. It supports over 40 programming languages.

## PythonMachineLearning - Practice and tutorial-style notebooks covering wide variety of machine learning techniques

•    Jupyter

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

## data-science-your-way - Ways of doing Data Science Engineering and Machine Learning in R and Python

•    Jupyter

These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python. We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.

•    Jupyter

## py - Repository to store sample python programs for python learning

•    Jupyter

Repository to store sample python programs for python learning

## pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data

•    Python

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. Binary installers for the latest released version are available at the Python package index and on conda.

## pandas-datareader - Extract data from a wide range of Internet sources into a pandas DataFrame.

•    HTML

Up to date remote data access for pandas, works for multiple versions of pandas. As of v0.6.0 Yahoo!, Google Options, Google Quotes and EDGAR have been immediately deprecated due to large changes in their API and no stable replacement.

## spark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

•    Jupyter

This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

## pycon-pandas-tutorial - PyCon 2015 Pandas tutorial materials

•    Jupyter

The first instance of this tutorial was delivered at PyCon 2015 in Montréal, but I hope that many other people will be able to benefit from it over the next few years — both on occasions on which I myself get to deliver it, and also when other instructors are able to do so. To make it useful to as many people as possible, I hereby release it under the MIT license (see the accompanying LICENSE.txt file) and I have tried to make sure that this repository contains all of the scripts needed to download and set up the data set that we used.

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